Python CyClpSimplex.solve方法代码示例

# 需要导入模块: from cylp.cy import CyClpSimplex [as 别名]
# 或者: from cylp.cy.CyClpSimplex import solve [as 别名]

#.........这里部分代码省略.........

    if use_cylp:
        sp = CyLPModel()
        u = sp.addVariable('u', A.shape[0], isInt = False)
        v = sp.addVariable('v', A.shape[0], isInt = False)
        u0 = sp.addVariable('u0', 1, isInt = False)
        v0 = sp.addVariable('v0', 1, isInt = False)
        alpha = sp.addVariable('alpha', lp.nVariables, isInt = False)
        beta = sp.addVariable('beta', 1, isInt = False)
    
        for i in range(A.shape[1]):
            sp += alpha[i] - sum(Atran[i,j]*u[j] for j in range(A.shape[0])) + pi[i]*u0 == 0
        for i in range(A.shape[1]):
            sp += alpha[i] - sum(Atran[i,j]*v[j] for j in range(A.shape[0])) - pi[i]*v0 == 0
        sp += beta - b*u + pi0*u0 <= 0
        sp += beta - b*v - (pi0 + 1)*v0 <= 0
        sp += u0 + v0 == 1
        if sense == '<=':
            sp += u >= 0
            sp += v >= 0
            sp += u0 >= 0
            sp += v0 >= 0
        else:
            #TODO this direction is not debugged
            # Is this all we need?
            sp += u <= 0
            sp += v <= 0
            sp += u0 <= 0
            sp += v0 <= 0
        sp.objective = sum(sol[i]*alpha[i] for i in range(A.shape[1])) - beta
        cbcModel = CyClpSimplex(sp).getCbcModel()
        cbcModel.logLevel = 0
        #cbcModel.maximumSeconds = 5
        cbcModel.solve()
        beta = cbcModel.primalVariableSolution['beta'][0]
        alpha = cbcModel.primalVariableSolution['alpha']
        u = cbcModel.primalVariableSolution['u']
        v = cbcModel.primalVariableSolution['v']
        u0 = cbcModel.primalVariableSolution['u0'][0]
        v0 = cbcModel.primalVariableSolution['v0'][0]
        if debug_print:
            print('Objective Value: ', cbcModel.objectiveValue)
            print('alpha: ', alpha, 'alpha*sol: ', np.dot(alpha, sol))
            print('beta: ', beta)
            print('Violation of cut: ',  np.dot(alpha, sol) - beta)
    else: 
        CG = AbstractModel()
        CG.u = Var(list(range(A.shape[0])), domain=NonNegativeReals,
                   bounds = (0.0, None))
        CG.v = Var(list(range(A.shape[0])), domain=NonNegativeReals,
                   bounds = (0.0, None))
        CG.u0 = Var(domain=NonNegativeReals, bounds = (0.0, None))
        CG.v0 = Var(domain=NonNegativeReals, bounds = (0.0, None))
        CG.alpha = Var(list(range(A.shape[0])), domain=Reals,
                       bounds = (None, None))    
        CG.beta  = Var(domain=Reals, bounds = (None, None))    
        
        ## Constraints
        def pi_rule_left(CG, i):
            x = float(pi[i])
            return(sum(Atran[i, j]*CG.u[j] for j in range(A.shape[0])) -
                   x*CG.u0 - CG.alpha[i] == 0.0)
        CG.pi_rule_left = Constraint(list(range(A.shape[1])), rule=pi_rule_left)
        
        def pi_rule_right(CG, i):
            x = float(pi[i])

# 需要导入模块: from cylp.cy import CyClpSimplex [as 别名]
# 或者: from cylp.cy.CyClpSimplex import solve [as 别名]
def splitCuts(lp, integerIndices = None, sense = '>=', sol = None,
              max_coeff = 1):
    A = lp.coefMatrix
    b = CyLPArray(lp.constraintsUpper)
    if integerIndices is None:
        integerIndices = range(lp.nVariables)
    if sol is None:
        sol = lp.primalVariableSolution['x']
    s = A*sol - b
    best = lp.getCoinInfinity()
    best_theta = None

    for theta in [0.1, 0.2, 0.3, 0.4, 0.5]:
        sp = CyLPModel()
        u = sp.addVariable('u', lp.nConstraints, isInt = False)
        v = sp.addVariable('v', lp.nConstraints, isInt = False)
        pi = sp.addVariable('pi', lp.nVariables, isInt = True)
        pi0 = sp.addVariable('pi0', 1, isInt = True)
    
        sp += pi + A.transpose()*u - A.transpose()*v == 0
        sp += pi0 + b*u - b*v == theta - 1
        if sense == '<=':
            sp += u >= 0
            sp += v >= 0
        else:
            #TODO this direction is not debugged
            # Is this all we need?
            sp += u <= 0
            sp += v <= 0
            
        sp.objective = (theta-1)*s*u - theta*s*v
        for i in xrange(lp.nVariables):
            if i in integerIndices:
                sp += -max_coeff <= pi[i] <= max_coeff
            else:
                sp[i] += pi[i] == 0

        cbcModel = CyClpSimplex(sp).getCbcModel()
        cbcModel.logLevel = 0
        #cbcModel.maximumSeconds = 5
        cbcModel.solve()
        if debug_print:
            print theta, cbcModel.objectiveValue
            print cbcModel.primalVariableSolution['pi'], 
            print cbcModel.primalVariableSolution['pi0']
        if cbcModel.objectiveValue < best:
            best = cbcModel.objectiveValue
            multu = cbcModel.primalVariableSolution['u']
            disjunction = cbcModel.primalVariableSolution['pi']
            rhs = cbcModel.primalVariableSolution['pi0']
            best_theta = theta
            
    if best_theta is not None:
        alpha = A.transpose()*multu + best_theta*disjunction
        if sense == '<=':
            beta = np.dot(lp.constraintsUpper, multu) + best_theta*rhs
        else:
            beta = np.dot(lp.constraintsLower, multu) + best_theta*rhs
        if (abs(alpha) > 1e-6).any():
            return [(alpha, beta)] 
    return []

# 需要导入模块: from cylp.cy import CyClpSimplex [as 别名]
# 或者: from cylp.cy.CyClpSimplex import solve [as 别名]
def maxViolationSplitCuts(lp, integerIndices = None, sense = '>=', sol = None,
              max_coeff = 1):
    #Warning: At the moment, you must put bound constraints in explicitly for split cuts
    A = lp.coefMatrix
    if sense == '<=':
        b = CyLPArray(lp.constraintsUpper)
    else:
        b = CyLPArray(lp.constraintsLower)
    if integerIndices is None:
        integerIndices = range(lp.nVariables)
    if sol is None:
        sol = lp.primalVariableSolution['x']
    s = A*sol - b
    best = lp.getCoinInfinity()
    best_theta = None

    for theta in [0.01, 0.02, 0.03, 0.04, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5]:
        sp = CyLPModel()
        u = sp.addVariable('u', lp.nConstraints, isInt = False)
        v = sp.addVariable('v', lp.nConstraints, isInt = False)
        pi = sp.addVariable('pi', lp.nVariables, isInt = True)
        pi0 = sp.addVariable('pi0', 1, isInt = True)
    
        sp += pi + A.transpose()*u - A.transpose()*v == 0
        sp += pi0 + b*u - b*v == theta - 1
        if sense == '<=':
            sp += u >= 0
            sp += v >= 0
        else:
            #TODO this direction is not debugged
            # Is this all we need?
            sp += u <= 0
            sp += v <= 0
            
        sp.objective = (theta-1)*s*u - theta*s*v
        for i in xrange(lp.nVariables):
            if i in integerIndices:
                sp += -max_coeff <= pi[i] <= max_coeff
            else:
                sp[i] += pi[i] == 0

        cbcModel = CyClpSimplex(sp).getCbcModel()
        cbcModel.logLevel = 0
        #cbcModel.maximumSeconds = 5
        cbcModel.solve()
        if debug_print:
            #print 'Theta: ', theta, 
            #print 'Objective Value: ', cbcModel.objectiveValue - theta*(1-theta)
            #print 'pi: ', cbcModel.primalVariableSolution['pi']
            #print 'pi0: ', cbcModel.primalVariableSolution['pi0']
            multu = cbcModel.primalVariableSolution['u']
            disjunction = cbcModel.primalVariableSolution['pi']
            rhs = cbcModel.primalVariableSolution['pi0']
            alpha = A.transpose()*multu + theta*disjunction
            beta = np.dot(b, multu) + theta*rhs
            #print 'alpha: ', alpha, 'alpha*sol: ', np.dot(alpha, sol)
            #print 'beta: ', beta
            #print 'Violation of cut: ',  np.dot(alpha, sol) - beta
        if cbcModel.objectiveValue - theta*(1-theta) < best:
            best = cbcModel.objectiveValue - theta*(1-theta)
            best_multu = cbcModel.primalVariableSolution['u']
            best_multv = cbcModel.primalVariableSolution['v']
            best_disjunction = cbcModel.primalVariableSolution['pi']
            best_rhs = cbcModel.primalVariableSolution['pi0']
            best_theta = theta
            
    if best_theta is not None:
        alpha = A.transpose()*best_multu + best_theta*best_disjunction
        beta = np.dot(b, best_multu) + best_theta*best_rhs
        if debug_print:
            print 'Violation of cut: ',  np.dot(alpha, sol) - beta
            print 'pi: ', best_disjunction
            print 'pi0: ', best_rhs
            print 'theta: ',  best_theta
        if (abs(alpha) > 1e-6).any():
            return [(alpha, beta)] 
    return []